AWS Data Engineer

AWS Data Engineer

We are seeking a highly skilled and motivated AWS Certified Engineer to design, build, and optimize scalable data solutions within the Amazon Web Services (AWS) ecosystem. The ideal candidate will have strong expertise in big data processing using PySpark and a deep understanding of data warehousing concepts, including Hive and modern table formats like Iceberg. This role involves developing, deploying, and managing robust, efficient, and secure data pipelines and analytics solutions on AWS, leveraging core networking and compute services.

Responsibilities:

• AWS Solution Design & Implementation: Design, develop, and deploy scalable and cost-effective data solutions on AWS, leveraging services such as S3 (for data lakes), EC2, EMR, Glue, Athena, Lambda, Redshift, and Kinesis.
• Data Pipeline Development: Build and maintain robust ETL/ELT data pipelines using PySpark for data ingestion, transformation, and loading into various data stores, including those utilizing open table formats like Iceberg.
• Big Data Processing: Develop and optimize big data processing jobs using PySpark on AWS EMR or AWS Glue, handling large datasets efficiently and integrating with Iceberg table formats.
• Data Warehousing: Design, implement, and manage data warehousing solutions, including schema design, data modeling, and query optimization, with a focus on Hive and modern data lake table formats like Iceberg for historical data and analytical queries.
• Cloud Infrastructure & Networking: Implement secure and robust cloud infrastructure components, including VPCs, subnets, routing, and security groups, to ensure proper connectivity and isolation for data solutions.
• Containerized Workloads: Design, deploy, and manage containerized data processing applications on Amazon Elastic Kubernetes Service (EKS).
• Performance Tuning & Optimization: Optimize AWS resources and big data applications (Spark, Hive, Iceberg) for performance, cost, and efficiency.
• Data Governance & Security: Implement best practices for data security, access control, and compliance within AWS, including IAM policies, S3 bucket policies, and encryption.
• Monitoring & Troubleshooting: Set up monitoring, alerting, and logging for data pipelines and AWS infrastructure; troubleshoot and resolve issues promptly.
• Automation: Develop and maintain automation scripts using Python and shell scripting for infrastructure provisioning, deployment, and operational tasks.
• Collaboration: Work closely with data scientists, analysts, and other engineering teams to understand data requirements and deliver reliable data solutions.

Qualifications:

• AWS Certification: Hold at least one AWS certification (e.g., AWS Certified Solutions Architect – Associate, AWS Certified Data Analytics – Specialty, AWS Certified Developer – Associate).
• AWS Services Expertise: Hands-on experience with key AWS services for data processing and storage including:
• Storage: S3 (for data lakes), EC2
• Data Processing: EMR, Glue, Athena, Lambda
• Networking: VPC, Subnets, Routing, Security Groups
• Containerization: EKS
• Big Data Processing: Strong proficiency in PySpark for developing complex data transformations and analytics.
• Data Lake Table Formats: Practical experience with Apache Iceberg for managing and querying data lakes.
• Data Warehousing: In-depth knowledge and practical experience with Apache Hive for data storage, querying, and schema management.

• Programming Languages:

• Python: Expert-level proficiency in Python for scripting, data manipulation, and AWS automation (Boto3).
• Shell Scripting: Proficient in shell scripting for automation and operational tasks.
• Database & SQL: Strong SQL skills for data querying and manipulation.
• Data Concepts: Solid understanding of ETL/ELT processes, data modeling, distributed computing, and data governance.

Good to Have Skills:

• Containerization Orchestration: Experience with Kubernetes for deploying and managing containerized applications.
• CI/CD: Experience with CI/CD tools and practices (e.g., AWS CodePipeline, GitHub Actions, GitLab CI) for automating deployment of data solutions.
• Orchestration: Experience with workflow orchestration tools like Apache Airflow.
• Version Control: Proficient in using Git for source code management.
• Other Big Data Technologies: Exposure to other big data technologies like Apache Kafka, Flink, or Presto.

Certifications:

• AWS Certified Solutions Architect – Associate/Professional
• AWS Certified Data Analytics – Specialty
• AWS Certified Developer – Associate